Algorithm based on one monocular video delivers highly valid and reliable gait parameters

Azhand, Arash; Rabe, Sophie; Müller, Swantje; Sattler, Igor; Steinert, Anika

doi:10.1038/s41598-021-93530-z

Cited by 40 publications

(38 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…3D monocular ankle joint angle differences during walking are between −10° and 10° for normal walking with maximal differences of 30° compared to marker-based methods. Two studies have examined temporospatial measures (step length, walking speed and cadence) using 2D monocular methods combined with projection mapping ( Shin et al, 2021 ) or a 3D musculoskeletal model ( Azhand et al, 2021 ), finding strong correlations when compared to the GAITRite pressure walkway ( Azhand et al, 2021 ; Shin et al, 2021 ). Therefore, while temporospatial measures may have sufficient accuracy for real world applications, significant improvements to identification of joint center location and angle are needed.…”

Section: Performance Of Current Markerless Applicationsmentioning

confidence: 99%

Applications and limitations of current markerless motion capture methods for clinical gait biomechanics

Wade

Needham

McGuigan

et al. 2022

PeerJ

View full text Add to dashboard Cite

Background Markerless motion capture has the potential to perform movement analysis with reduced data collection and processing time compared to marker-based methods. This technology is now starting to be applied for clinical and rehabilitation applications and therefore it is crucial that users of these systems understand both their potential and limitations. This literature review aims to provide a comprehensive overview of the current state of markerless motion capture for both single camera and multi-camera systems. Additionally, this review explores how practical applications of markerless technology are being used in clinical and rehabilitation settings, and examines the future challenges and directions markerless research must explore to facilitate full integration of this technology within clinical biomechanics. Methodology A scoping review is needed to examine this emerging broad body of literature and determine where gaps in knowledge exist, this is key to developing motion capture methods that are cost effective and practically relevant to clinicians, coaches and researchers around the world. Literature searches were performed to examine studies that report accuracy of markerless motion capture methods, explore current practical applications of markerless motion capture methods in clinical biomechanics and identify gaps in our knowledge that are relevant to future developments in this area. Results Markerless methods increase motion capture data versatility, enabling datasets to be re-analyzed using updated pose estimation algorithms and may even provide clinicians with the capability to collect data while patients are wearing normal clothing. While markerless temporospatial measures generally appear to be equivalent to marker-based motion capture, joint center locations and joint angles are not yet sufficiently accurate for clinical applications. Pose estimation algorithms are approaching similar error rates of marker-based motion capture, however, without comparison to a gold standard, such as bi-planar videoradiography, the true accuracy of markerless systems remains unknown. Conclusions Current open-source pose estimation algorithms were never designed for biomechanical applications, therefore, datasets on which they have been trained are inconsistently and inaccurately labelled. Improvements to labelling of open-source training data, as well as assessment of markerless accuracy against gold standard methods will be vital next steps in the development of this technology.

show abstract

Section: Performance Of Current Markerless Applicationsmentioning

confidence: 99%

Applications and limitations of current markerless motion capture methods for clinical gait biomechanics

Wade

Needham

McGuigan

et al. 2022

PeerJ

View full text Add to dashboard Cite

show abstract

“…It also does not require tuning heuristics to determine step times from 2D keypoint velocities. Another study used a similar pipeline from 2D keypoints acquired in the frontal plane to lifted 3D joint locations [34]. In contrast to our gait transformer which directly outputs kinematic parameters, they optimize a skeleton biomechanical fit to the keypoints, which is used to estimate the gait parameters.…”

Section: Discussionmentioning

confidence: 99%

Transforming Gait: Video-Based Spatiotemporal Gait Analysis

Cotton¹,

Emoonah²,

Anthony³

et al. 2022

Preprint

View full text Add to dashboard Cite

Human pose estimation from monocular video is a rapidly advancing field that offers great promise to human movement science and rehabilitation. This potential is tempered by the smaller body of work ensuring the outputs are clinically meaningful and properly calibrated. Gait analysis, typically performed in a dedicated lab, produces precise measurements including kinematics and step timing. Using over 7000 monocular video from an instrumented gait analysis lab, we trained a neural network to map 3D joint trajectories and the height of individuals onto interpretable biomechanical outputs including gait cycle timing and sagittal plane joint kinematics and spatiotemporal trajectories. This task specific layer produces accurate estimates of the timing of foot contact and foot off events. After parsing the kinematic outputs into individual gait cycles, it also enables accurate cycle-by-cycle estimates of cadence, step time, double and single support time, walking speed and step length.

show abstract

“…Object detection is achieved by the method of boundary structure segmentation, which has a good detection effect on translation and image deformation. Literature [ 18 ] uses a novel shape descriptor called the chord operator to describe objects.…”

Section: Related Workmentioning

confidence: 99%

The Visual Movement Analysis of Physical Education Teaching considering the Generalized Hough Transform Model

Liu

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

In order to promote the effect of college physical education reform, this paper combines the generalized Hough transform model to analyze the visual movement of physical education teaching. The idea proposed in this paper uses the position information of the edge image itself and the direction information of the curve segment to directly eliminate the impossible targets, which fundamentally alleviates the problem of invalid sampling and accumulation. Moreover, this paper greatly constrains the parameter space based on the results of each segment of the curve, which greatly reduces the search burden of high-dimensional parameters, and combines the improved algorithm to construct a sports teaching video action analysis system. The experimental research shows that the visual movement analysis system of physical education teaching considering the generalized Hough transform model proposed in this paper can effectively analyze the sports teaching actions and improve the efficiency of physical education.

show abstract

Algorithm based on one monocular video delivers highly valid and reliable gait parameters

Cited by 40 publications

References 58 publications

Applications and limitations of current markerless motion capture methods for clinical gait biomechanics

Applications and limitations of current markerless motion capture methods for clinical gait biomechanics

Transforming Gait: Video-Based Spatiotemporal Gait Analysis

The Visual Movement Analysis of Physical Education Teaching considering the Generalized Hough Transform Model

Contact Info

Product

Resources

About